3 Key Differences between AI and Machine Learning

By : |February 1, 2019 0

Today, we see AI and Machine Learning being used interchangeably in many articles and even common talk. We keep hearing people say AI/ML in one breath which may have led a lot of you to believe they are the same. However, that’s far from actual meaning of these terms.

Ankur Sharma, VP, Analytics, Instamojo, shared his opinion about the key differences between AI and ML.

Artificial Intelligence: Giving machines or computers a cognitive intelligence – ability to think intelligently, just like humans.

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Machine Learning: The science of making machines learn from past data and actions taken by themselves, using statistical patterns.

So, Machine Learning is more like a means to achieve Artificial Intelligence, which covers much broader fields of computer science applications.

Key Difference 1:

AI relies not just on software applications but also hardware components such as cameras, microphones, GPS, microprocessors of different capacities. All these components work together, along with machine learning algorithms, to transform into an intelligent agent which aspires to do one or more specific activities just like a human would do (sometimes even better than humans).

Machine Learning applications are algorithms running inside a system – these algorithms learn from data fed to them by humans or other applications, and get better with more data or more time. Some algorithms learn once, some periodically, and some of them learn continuously, depending on the applications.

Key Difference 2:

Setting up fully-developed Artificial Intelligence labs/products would need different kinds of expertise in a team – including the likes of hardware engineering, application development, data science, and distributed computing.

On the other hand, an expertise in data science/statistics/data analysis is the main requirement in building machine learning based solutions.

Key Difference 3:

Artificial Intelligence applications are mostly independent and end-to-end, whereas Machine Learning applications can also be built on top of existing systems and processes.

Examples of Artificial Intelligence Applications: Self-Driving Cars, Intelligent Home Devices, Smart Lights and Speakers, Health Diagnostics Robots, Robotic cleaners, Game Players.

Examples of Machine Learning Applications: Fraud Detection, Recommendation Systems, Lead Qualifiers, Marketing Personalization, Face Detection.

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